{"id":"https://openalex.org/W2768009948","doi":"https://doi.org/10.1145/3132847.3133006","title":"Region Representation Learning via Mobility Flow","display_name":"Region Representation Learning via Mobility Flow","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2768009948","doi":"https://doi.org/10.1145/3132847.3133006","mag":"2768009948"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3133006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100740070","display_name":"Hongjian Wang","orcid":"https://orcid.org/0000-0002-7918-4548"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongjian Wang","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016516907","display_name":"Zhenhui Li","orcid":"https://orcid.org/0000-0001-7221-2588"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenhui Li","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100740070"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":22.8384,"has_fulltext":false,"cited_by_count":113,"citation_normalized_percentile":{"value":0.99453572,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"237","last_page":"246"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.767597496509552},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6345556974411011},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49703148007392883},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4840785562992096},{"id":"https://openalex.org/keywords/control-flow-graph","display_name":"Control flow graph","score":0.44739073514938354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4395098388195038},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.42322033643722534},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.39007893204689026},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3900589644908905},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3518078327178955},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11147263646125793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.767597496509552},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6345556974411011},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49703148007392883},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4840785562992096},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.44739073514938354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4395098388195038},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.42322033643722534},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39007893204689026},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3900589644908905},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3518078327178955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11147263646125793},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3133006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8500000238418579,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1035053891","display_name":null,"funder_award_id":"1639150","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4098986844","display_name":null,"funder_award_id":"1652525","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4342645261","display_name":null,"funder_award_id":"1618448","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G70512371","display_name":null,"funder_award_id":"1544455","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1888005072","https://openalex.org/W1971402834","https://openalex.org/W1988134474","https://openalex.org/W2001141328","https://openalex.org/W2023596329","https://openalex.org/W2024859348","https://openalex.org/W2138972650","https://openalex.org/W2141599568","https://openalex.org/W2142535891","https://openalex.org/W2153207204","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2162873750","https://openalex.org/W2165178985","https://openalex.org/W2250539671","https://openalex.org/W2514525802","https://openalex.org/W2534727297","https://openalex.org/W2538371562","https://openalex.org/W2962756421","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W6691431627"],"related_works":["https://openalex.org/W2062195135","https://openalex.org/W2795079307","https://openalex.org/W2793058541","https://openalex.org/W1983629434","https://openalex.org/W2055929693","https://openalex.org/W2964145245","https://openalex.org/W2595205408","https://openalex.org/W4386136067","https://openalex.org/W4286858940","https://openalex.org/W2422195048"],"abstract_inverted_index":{"Increasing":[0],"amount":[1],"of":[2,30,127],"urban":[3,18,22],"data":[4,120],"are":[5,36],"being":[6],"accumulated":[7],"and":[8,20,28,62,85,104,116],"released":[9],"to":[10,15,68,80,95],"public;":[11],"this":[12,33],"enables":[13],"us":[14,54],"study":[16],"the":[17,45,57,63,71,90,98,119,125],"dynamics":[19,84],"address":[21],"issues":[23],"such":[24],"as":[25],"crime,":[26],"traffic,":[27],"quality":[29],"living.":[31],"In":[32],"paper,":[34],"we":[35,78],"interested":[37],"in":[38,88],"learning":[39,89],"vector":[40],"representations":[41,51,99],"for":[42],"regions":[43],"using":[44,130],"large-scale":[46],"taxi":[47],"flow":[48,102],"data.":[49],"These":[50],"could":[52,112],"help":[53],"better":[55,69],"measure":[56],"relationship":[58],"strengths":[59],"between":[60],"regions,":[61],"relationships":[64],"can":[65],"be":[66],"used":[67],"model":[70],"region":[72,91],"properties.":[73],"Different":[74],"from":[75,100],"existing":[76],"studies,":[77],"propose":[79,94],"consider":[81],"both":[82],"temporal":[83],"multi-hop":[86],"transitions":[87],"representations.":[92],"We":[93,123],"jointly":[96],"learn":[97],"a":[101,105,109],"graph":[103,111],"spatial":[106],"graph.":[107],"Such":[108],"combined":[110],"simulate":[113],"individual":[114],"movements":[115],"also":[117],"addresses":[118],"sparsity":[121],"issue.":[122],"demonstrate":[124],"effectiveness":[126],"our":[128],"method":[129],"three":[131],"different":[132],"real":[133],"datasets.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":9}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}
